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fix 4.x links

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Alexander Alekhin
2021-12-22 13:01:26 +00:00
parent be110d0464
commit c78a8dfd2d
134 changed files with 334 additions and 332 deletions
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doc/tutorials/dnn/dnn_pytorch_tf_detection/tf_det_model_conversion_tutorial.md
+3 -3
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@@ -27,7 +27,7 @@ source <env_dir_path>/bin/activate
For OpenCV-Python building from source, follow the corresponding instructions from the @ref tutorial_py_table_of_contents_setup.
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
Before you start the installation of the libraries, you can customize the [requirements.txt](https://github.com/opencv/opencv/tree/4.x/samples/dnn/dnn_model_runner/dnn_conversion/requirements.txt), excluding or including (for example, ``opencv-python``) some dependencies.
The below line initiates requirements installation into the previously activated virtual environment:
```console
@@ -98,7 +98,7 @@ To provide model inference we will use the below [double-decker bus photo](https
To initiate the test process we need to provide an appropriate model configuration. We will use [``ssd_mobilenet_v1_coco.config``](https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_mobilenet_v1_coco.config) from [TensorFlow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection#tensorflow-object-detection-api).
TensorFlow Object Detection API framework contains helpful mechanisms for object detection model manipulations.
We will use this configuration to provide a text graph representation. To generate ``.pbtxt`` we will use the corresponding [``samples/dnn/tf_text_graph_ssd.py``](https://github.com/opencv/opencv/blob/master/samples/dnn/tf_text_graph_ssd.py) script:
We will use this configuration to provide a text graph representation. To generate ``.pbtxt`` we will use the corresponding [``samples/dnn/tf_text_graph_ssd.py``](https://github.com/opencv/opencv/blob/4.x/samples/dnn/tf_text_graph_ssd.py) script:
```console
python tf_text_graph_ssd.py --input ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb --config ssd_mobilenet_v1_coco_2017_11_17/ssd_mobilenet_v1_coco.config --output ssd_mobilenet_v1_coco_2017_11_17.pbtxt
@@ -106,7 +106,7 @@ python tf_text_graph_ssd.py --input ssd_mobilenet_v1_coco_2017_11_17/frozen_infe
After successful execution ``ssd_mobilenet_v1_coco_2017_11_17.pbtxt`` will be created.
Before we run ``object_detection.py``, let's have a look at the default values for the SSD MobileNetV1 test process configuration. They are located in [``models.yml``](https://github.com/opencv/opencv/blob/master/samples/dnn/models.yml):
Before we run ``object_detection.py``, let's have a look at the default values for the SSD MobileNetV1 test process configuration. They are located in [``models.yml``](https://github.com/opencv/opencv/blob/4.x/samples/dnn/models.yml):
```yml
ssd_tf: